
"""simple docstring"""


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Optional[int]          =    MvpTokenizer
				a__:      List[str]          =    MvpTokenizerFast
				a__:      Dict          =    True
				a__:      Any          =    filter_roberta_detectors




				def        UpperCAmelCase__						(				self   ):
							super().setUp()
							lowerCamelCase_					=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							lowerCamelCase_					=  dict(zip(UpperCAmelCase							,						range(len(UpperCAmelCase   )   )   )   )
							lowerCamelCase_					=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							lowerCamelCase_					=  {'''unk_token''': '''<unk>'''}

							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''merges_file''']   )
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write(json.dumps(UpperCAmelCase   ) + '''\n'''   )
							with open(self.merges_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										fp.write('''\n'''.join(UpperCAmelCase   )   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return "lower newer", "lower newer"




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''   )




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							lowerCamelCase_					=  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						max_length=len(UpperCAmelCase   )							,						padding=UpperCAmelCase							,						return_tensors='''pt'''   )
										self.assertIsInstance(UpperCAmelCase							,						UpperCAmelCase   )

										self.assertEqual((2, 9)							,						batch.input_ids.shape   )
										self.assertEqual((2, 9)							,						batch.attention_mask.shape   )
										lowerCamelCase_					=  batch.input_ids.tolist()[0]
										self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )
										# Test that special tokens are reset




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding=UpperCAmelCase							,						return_tensors='''pt'''   )
										# check if input_ids are returned and no labels
										self.assertIn('''input_ids'''							,						UpperCAmelCase   )
										self.assertIn('''attention_mask'''							,						UpperCAmelCase   )
										self.assertNotIn('''labels'''							,						UpperCAmelCase   )
										self.assertNotIn('''decoder_attention_mask'''							,						UpperCAmelCase   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [
							    '''Summary of the text.''',
							    '''Another summary.''',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(text_target=UpperCAmelCase							,						max_length=32							,						padding='''max_length'''							,						return_tensors='''pt'''   )
										self.assertEqual(32							,						targets['''input_ids'''].shape[1]   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(
										    ['''I am a small frog''' * 1024, '''I am a small frog''']							,						padding=UpperCAmelCase							,						truncation=UpperCAmelCase							,						return_tensors='''pt'''   )
										self.assertIsInstance(UpperCAmelCase							,						UpperCAmelCase   )
										self.assertEqual(batch.input_ids.shape							,						(2, 1024)   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ['''A long paragraph for summarization.''']
							lowerCamelCase_					=  [
							    '''Summary of the text.''',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						text_target=UpperCAmelCase							,						return_tensors='''pt'''   )
										lowerCamelCase_					=  inputs['''input_ids''']
										lowerCamelCase_					=  inputs['''labels''']
										self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
										self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
										self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )




				def        UpperCAmelCase__						(				self   ):
							pass





				def        UpperCAmelCase__						(				self   ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
													lowerCamelCase_					=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase							,						**UpperCAmelCase   )
													lowerCamelCase_					=  self.tokenizer_class.from_pretrained(UpperCAmelCase							,						**UpperCAmelCase   )
													lowerCamelCase_					=  '''A, <mask> AllenNLP sentence.'''
													lowerCamelCase_					=  tokenizer_r.encode_plus(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_token_type_ids=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer_p.encode_plus(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase							,						return_token_type_ids=UpperCAmelCase   )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r['''token_type_ids''']   )							,						sum(tokens_p['''token_type_ids''']   )   )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )							,						sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )							,						)

													lowerCamelCase_					=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
													lowerCamelCase_					=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p['''input_ids''']							,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
													self.assertSequenceEqual(tokens_r['''input_ids''']							,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )

													self.assertSequenceEqual(
													    UpperCAmelCase							,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
													self.assertSequenceEqual(
													    UpperCAmelCase							,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )







'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b == 0:
								return 1
				if (b % 2) == 0:
								return actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)
				else:
								return a * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b < 0:
								return 1 / actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				print(power(-2, -3))




